paddlenlp.transformers.t5.tokenizer 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import warnings
import re

import sentencepiece as spm

from ..albert.tokenizer import AlbertEnglishTokenizer

__all__ = ['T5Tokenizer', ]

[文档]class T5Tokenizer(AlbertEnglishTokenizer): """ Constructs a T5 tokenizer based on SentencePiece . This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: sentencepiece_model_file (str): The vocabulary file (ends with '.spm') required to instantiate a `SentencePiece <>`__ tokenizer. do_lower_case (bool): Whether or not to lowercase the input when tokenizing. Defaults to `False`. remove_space (bool): Whether or note to remove space when tokenizing. Defaults to `True`. keep_accents (bool): Whether or note to keep accents when tokenizing. Defaults to `False`. eos_token (str): A special token representing the *eos (end-of-sentence)* token. Defaults to "</s>". unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "<unk>". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "<pad>". """ resource_files_names = {"sentencepiece_model_file": "spiece.model"} pretrained_resource_files_map = { "sentencepiece_model_file": { "t5-small": "", "t5-base": "", "t5-large": "", "t5-v1_1-base": "", "t5-v1_1-large": "", }, } pretrained_init_configuration = { "t5-small": { "do_lower_case": False }, "t5-base": { "do_lower_case": False }, "t5-large": { "do_lower_case": False }, "t5-v1_1-base": { "do_lower_case": False }, "t5-v1_1-large": { "do_lower_case": False }, } def __init__(self, sentencepiece_model_file, do_lower_case=False, remove_space=True, keep_accents=True, eos_token="</s>", unk_token="<unk>", pad_token="<pad>", extra_ids=100, additional_special_tokens=[], **kwargs): # Add extra_ids to the special token list if extra_ids > 0 and len(additional_special_tokens) == 0: self._additional_special_tokens = [ f"<extra_id_{i}>" for i in range(extra_ids) ] elif extra_ids > 0 and len(additional_special_tokens) != 0: # Check that we have the right number of extra_id special tokens extra_tokens = len( set( filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))) if extra_tokens != extra_ids: raise ValueError( f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to T5Tokenizer. " "In this case the additional_special_tokens must include the extra_ids tokens" ) self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.extra_ids = extra_ids self.sentencepiece_model_file = sentencepiece_model_file self.sp_model = spm.SentencePieceProcessor() self.sp_model.Load(sentencepiece_model_file) def __call__(self, text, text_pair=None, max_seq_len=None, stride=0, is_split_into_words=False, pad_to_max_seq_len=False, truncation_strategy="longest_first", return_position_ids=False, return_token_type_ids=False, return_attention_mask=True, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False): return super(T5Tokenizer, self).__call__( text, text_pair, max_seq_len, stride, is_split_into_words, pad_to_max_seq_len, truncation_strategy, return_position_ids, return_token_type_ids, return_attention_mask, return_length, return_overflowing_tokens, return_special_tokens_mask) @property def vocab_size(self): return len(self.sp_model) + self.extra_ids def _add_eos_if_not_present(self, token_ids): """Do not add eos again if user already added it.""" if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added." ) return token_ids else: return token_ids + [self.eos_token_id]
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1): """ Build model inputs from a sequence or a pair of sequence. An Reformer sequence has the following format: - single sequence: ``X </s>`` - pair of sequences: ``A </s> B </s>`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of input_id with the appropriate special tokens. """ token_ids_0 = self._add_eos_if_not_present(token_ids_0) if token_ids_1 is None: return token_ids_0 else: token_ids_1 = self._add_eos_if_not_present(token_ids_1) return token_ids_0 + token_ids_1
[文档] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences. If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Returns: List[int]: List of token_type_id according to the given sequence(s). """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): List of ids of the first sequence. token_ids_1 (List[int], optional): List of ids of the second sequence. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: List[int]: The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True, ) # normal case: some special tokens if token_ids_1 is None: return ([0] * len(token_ids_0)) + [1] return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
[文档] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += (self.sp_model.decode_pieces(current_sub_tokens) + token + " ") current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode_pieces(current_sub_tokens) return out_string.strip()
[文档] def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True): """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``. Args: token_ids (Union[List[int], Tensor]): List of tokenized input ids. skip_special_tokens (bool, optional): Whether or not to remove special tokens in the decoding. Defaults to `False`. clean_up_tokenization_spaces (bool, optional): Whether or not to clean up the tokenization spaces. Defaults to `True`. Returns: str: The decoded sentence. """ if hasattr(token_ids, "tolist"): token_ids = token_ids.tolist() text = self.convert_tokens_to_string( self.convert_ids_to_tokens( token_ids, skip_special_tokens=skip_special_tokens)) if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) return text
def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token.startswith("<extra_id_"): match = re.match(r"<extra_id_(\d+)>", token) num = int( return self.vocab_size - num - 1 return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index < self.sp_model.get_piece_size(): token = self.sp_model.IdToPiece(index) else: token = f"<extra_id_{self.vocab_size - 1 - index}>" return token
[文档] def batch_decode(self, sequences, skip_special_tokens=False, clean_up_tokenization_spaces=True): """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (Union[List[int], List[List[int]], Tensor]): List of tokenized input ids. skip_special_tokens (bool, optional): Whether or not to remove special tokens in the decoding. Defaults to `False`. clean_up_tokenization_spaces (bool, optional): Whether or not to clean up the tokenization spaces. Defaults to `True`. Returns: List[str]: The list of decoded sentences. """ return [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces) for seq in sequences ]
[文档] @staticmethod def clean_up_tokenization(out_string): """ Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms. Args: out_string (str): The text to clean up. Returns: str: The cleaned-up string. """ out_string = (out_string.replace(" .", ".").replace(" ?", "?") .replace(" !", "!").replace(" ,", ",").replace(" ' ", "'") .replace(" n't", "n't").replace(" 'm", "'m") .replace(" 's", "'s").replace(" 've", "'ve") .replace(" 're", "'re")) return out_string